Year: 2025
Type: article
Abstract: Purpose This study introduces a novel fuzzy multi-attribute analysis (FMAA) model to enhance decision-making in engineering-procurement-construction (EPC) contractor selection by effectively addressing the complexities and uncertainties inherent in these processes. Design/methodology/approach The FMAA model was developed through a comprehensive methodology comprising an extensive literature review to identify challenges and criteria in contractor selection, expert consultations to refine the model framework, and validation via in-depth interviews with experienced EPC project managers. Key steps include defining selection criteria, determining appropriate weights, and employing fuzzy set theory to manage uncertainties. Findings Compared to traditional methods, the FMAA model offers a structured and scalable approach to contractor selection, effectively reducing uncertainty and enhancing decision-making accuracy. Validation through expert interviews confirmed its practical relevance and adaptability across diverse EPC project contexts. Research limitations/implications While the model provides valuable insights, further research utilizing quantitative validation methods and case studies is essential to assess its applicability across various construction sectors and geographical regions. Practical implications By adopting the FMAA model, project managers can systematically evaluate contractors, ensuring the selection of candidates best suited to achieve long-term project success and sustainability. Social implications Structured decision-making frameworks like the FMAA model promote improved project outcomes and sustainable practices, contributing to enhanced performance within the construction industry. Originality/value This study advances contractor selection methodologies by integrating contemporary decision-making approaches with empirical validation, addressing critical gaps in managing the complexity and uncertainty inherent in EPC projects.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4410674142